Systems and methods for a turbo low-density parity-check decoder

ABSTRACT

A method for forming a plurality of parity check matrices for a plurality of data rates for use in a Low-Density Parity-Check (LDPC) decoder, comprises establishing a first companion exponent matrix corresponding to a first parity check matrix for a first data rate, and partitioning the first parity check matrix and the first companion exponent matrix into sub-matrices such that the first parity check matrix is defined using a cyclical shift of an identity matrix.

RELATED APPLICATION INFORMATION

This application claims priority under 35 U.S.C. 119(e) to U.S. Provisional Application Ser. No. 60/705,277, entitled “Turbo LDPC,” filed Aug. 3, 2005, which is incorporated herein by reference in its entirety as if set forth in full.

BACKGROUND

1. Field of the Invention

The embodiments described herein are related to methods for Low-Density Parity-Check decoding and more particularly to methods for achieving efficient and cost-effective Low-Density Parity-Check decoders.

2. Background of the Invention

A Low-Density Parity-Check (LDPC) code is an error correcting code that provides a method for transferring a message over a noisy transmission channel. While LDPC techniques cannot guaranty perfect transmission, they can be used to make the probability of lost information very small. In fact, LDPC codes were the first to allow data transmission rates at close to the theoretical maximum, e.g., the Shannon Limit. LDPC techniques use a sparse parity-check matrix, e.g, a matrix populated mostly with zeros; hence the term low-density. The sparse matrix is randomly generated subject to defined sparsity constraint.

LDPC codes can be defined as both the matrix and in graphical form. A LDPC matrix will have a certain number of rows (N) and columns (M). The matrix can also be defined by the number of 1's in each row (w_(r)) and the number of 1's in each column (w_(c)). For a matrix to be considered low-density, the following conditions should be met: w_(c)<<N and w_(r)<<M. A LDPC matrix can also be regular or irregular. A regular LDPC matrix, or code is one in which w_(c) is constant for every column and w_(r)=w_(c)*(N/M) is also constant for every row. If the matrix is low-density but the number of 1's in each row or column are not constant, then such codes are called irregular LDPC codes.

It will also be understood that an LDPC code can be graphically defined by its corresponding Tanner graph. Not only do such graphs provide a complete representation of the code, they also help to describe the decoding algorithm as explained in more detail below. The nodes of the graph are separated into two distinctive sets and edges are only connecting nodes of two different types. The two types of nodes in a Tanner graph are called the variable nodes (v-nodes) and check nodes (c-nodes). Thus, the Tanner graph will consist of M check nodes (the number of parity bits) and N variable nodes (the number of bits in a code word). A check node will then be connected to a variable node if there is a 1 in the corresponding element of the LDPC matrix.

The number of information bits can be represented as (K). Accordingly, the number of parity check bits M=N−K. A Generator Matrix (G_(N×K)) can then be defined according to the following:

G _(N×K) =c _(N×1) /d _(K×1) or

c _(N×1) =G _(N×K) d _(K×1), where

-   -   d_(K×1)=a message or date word, and     -   c_(N×1)=a code word.

As can be seen, the code word c_(N×1) is generated by multiplying the message by the generator matrix. The subscripts are matrix rotation and refer to the number of rows and columns respectfully. Thus, the data word and code word can be represented as single column matrices with K and N rows respectfully.

The parity check Matrix can be defined as H_(M×N)c_(N×1)=0.

Accordingly, FIG. 1 is a diagram illustrating a system 100 that includes a transmitter and a receiver. A portion 102 of the transmitter and a portion 110 of the receiver are shown for simplicity. Referring to FIG. 1, an encoder 104 converts a data word d_(K×1) into a code word c_(N×1) via application of the generator matrix G_(N×K). Modulator 106 can be configured to then modulate code word c_(N×1) onto a carrier so that the code word can be wirelessly transmitted across channel 108 to the receiver.

In receive portion 110, demodulator 112 can be configure to remove the carrier from the received signal; however, channel 108 will add channel effects and noise, such the signal produced by demodulator 112 can have the form: r_(N×1)=2/σ²(1−2 c_(N×1))+w_(N×1), where r is a multilevel signal. As a result of the noise and channel effects, some of data bits d will be lost in the transmission. In order to recover as much of the data as possible, decoder 114 can be configured to use the parity check matrix G_(M×N) to produce an estimate d′_(K×1) of the data that is very close to the original data d_(K×1). It will be understood that decoder 114 can be a hard decision decoder or a soft decision decoder. Soft decision decoders are more accurate, but also typically require more resources.

Unfortunately, conventional LDPC decoding techniques result in a high complexity, fully parallel decoder implementations where all the messages to and from all the parity node processors have to be computed at every iteration in the decoding process. This leads to large complexity, increased research requirements, and increased cost. Serializing part of the decoder by sharing a number of parity node processors is one option for reducing some of the overhead involved; however, serializing part of the decoder would result in stringent memory requirements to store the messages in an interconnection complexity bottleneck, i.e., complex interconnects between variable node, processors and parity node processors. Accordingly, serializing part of the decoder is not solely to solve the problems above.

Further, if different data rates are to be supported then the decoder becomes even more complex in terms of memory size, memory architecture, and interconnect complexity. In general, another problem with conventional LDPC decoders is that the computation performed by the parity node processors are highly complex. Accordingly, this computations limit the speed of the decoder and increase its size and cost.

SUMMARY

A LDPC decoder comprises a highly structured parity-check matrix that supports variable rates, while still maintaining limited complexity. The LDPC decoder implements resource sharing that reduces the number of parity node processors in a highly efficient manner. The LDPC encoder also comprises an efficient and small memory architecture and reduces interconnect complexity.

In one aspect, compression and decompression algorithms are used to store and retrieve messages from memory.

These and other features, aspects, and embodiments of the invention are described below in the section entitled “Detailed Description.”

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and embodiments of the inventions are described in conjunction with the attached drawings, in which:

FIG. 1 is a diagram illustrating an example communication system that uses LDPC codes;

FIG. 2 is a diagram illustrating the operation of an exemplary parity check matrix;

FIG. 3 is a illustrating the operation of an exemplary parity node processor;

FIG. 4 is a diagram illustrating the operation of an exemplary variable node processor;

FIG. 5 is a diagram illustrating an example exponential matrix configured in accordance with one embodiment;

FIG. 6 is a diagram illustrating an example parity check matrix in accordance with one embodiment;

FIG. 7 is a diagram illustrating an example sub-matrix used to construct the parity check matrix of FIG. 6 in accordance with one embodiment;

FIG. 8 is a diagram illustrating further sub-matrices used to construct the parity check matrix of FIG. 6 in accordance with one embodiment;

FIG. 9 is a diagram illustrating further sub-matrices that includes the sub-matrices of FIG. 8 used to construct the parity check matrix of FIG. 6 in accordance with one embodiment;

FIGS. 10-15 are diagrams illustrating the patterns used to form the sub matrices of FIG. 9;

FIG. 16 is a diagram illustrating the formation of a sub-matrix using one of the patterns illustrated in FIGS. 10-15;

FIG. 17 is a diagram illustrating an exponential matrix constructed in accordance with one embodiment;

FIG. 18 is a diagram illustrating the construction of an exponential sub-matrix in accordance with one embodiment;

FIG. 19 is a diagram illustrating a parity check matrix for a ¾ data rate constructed in accordance with one embodiment;

FIG. 20 is a diagram illustrating the construction of an exponential sub-matrix in accordance with one embodiment; and

FIG. 21 is a diagram illustrating a parity check matrix for a ⅞ data rate constructed in accordance with one embodiment.

DETAILED DESCRIPTION

In the descriptions that follow, certain example parameters, values, etc., are used; however, it will be understood that the embodiments described herein are not necessarily limited by these examples. Accordingly, these examples should not be seen as limiting the embodiments in any way. Further, the embodiments of an LDPC decoder described herein can be applied to many different types of systems implementing a variety of protocols and communication techniques. Accordingly, the embodiments should not be seen as limited to a specific type of system, architecture, protocol, air interface, etc. unless specified.

In order to illustrate the operation of LDPC codes, the following example:

$H_{3 \times 6} = \begin{bmatrix} 1 & 0 & 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 1 \\ 1 & 1 & 0 & 0 & 0 & 1 \end{bmatrix}$

As can be seen, the example parity check matrix H is low density, or sparse. The first row of matrix H defines the first parity check node, or equation. As can be seen, the first parity check node will check received samples r₀, r₂, and r₄, remembering that r is the multilevel signal produced by demodulator 112 in the receiver. The second parity check node, i.e., the second row of H, checks for received samples r₁, r₃, and r₅, and the third parity check node checks samples r₀, r₁, and r₅. In this example, there are three parity check nodes and six samples. The first and second parity check nodes are considered orthogonal, because they involve mutually exclusive sets of samples.

If it is assumed that K=3 and M=3, then the following is true:

${H_{3 \times 6}c_{6 \times 1}} = {\left. 0\Leftrightarrow{H_{3 \times 6}\begin{bmatrix} d_{3 \times 1} \\ p_{3 \times 1} \end{bmatrix}} \right. = {\left. 0\Leftrightarrow{\begin{bmatrix} 1 & 0 & 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 1 \\ 1 & 1 & 0 & 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} d_{0} \\ d_{1} \\ d_{2} \\ p_{0} \\ p_{1} \\ p_{2} \end{bmatrix}} \right. = 0}}$

This produces the following equations:

d ₀ +d ₂ +p ₁=0

d ₁ +p ₀ +p ₂=0

d ₀ +d ₁ +p ₂=0

These equations reduce to:

p₀=d₀

p ₁ =d ₀ +d ₂

p ₂ =d ₀ +d ₁

Thus, for example, if d=[0;1;0], then p=[0;0;1] and c=[0;1;0;0;0;1].

FIG. 2 is a diagram illustrating the operation of H in the example above. As can be seen, the graph of FIG. 2 has three parity check nodes 202, 204, and 206, and 6 variable nodes 208, 210, 212, 214, 216, and 218, which correspond to the bits of c. Parity check nodes 202, 204, and 206 are connected with variable nodes 208, 210, 212, 214, 216, and 218, via edges 220, 222, 224, 226, 228, 230, 232, 234, and 236 as dictated by the entries in H. In other words, each edge 220, 222, 224, 226, 228, 230, 232, 234, and 236 should correspond to a 1 in H.

In an LDPC decoder, the operations of the parity check and variable nodes can be implemented by processors. In other words, each parity check node can be implemented by a parity check processor, and each variable check node can be implemented by a variable node processor. An LDPC is then an iterative decoder that implements a message passing algorithm defined by H.

FIG. 3 is a diagram illustrating the operation of parity node processor 202. First, the LDPC decoder will initialize the variable data bits v₀, v₁, v₂ . . . v₆ of variable node processors 208, 210, 212, 214, 216, and 218 with r₀, r₁, r₂, . . . r₆. Referring to FIG. 3, v₀ ^(k-1), v₂ ^(k-1), and v₄ ^(k-1) are the variable messages sent from variable nodes 208, 210, 212, 214, 216, and 218 to parity node processor 202. Parity node processor 202 operates on these messages and computes its messages E^(k). For example, E^(k)(0→2) represents the message sent form parity node 202 to variable node 212 at the kth iteration.

The messages produced by parity node processor 202 can be defined using the following equations:

${E^{k}\left( 0\rightarrow 0 \right)} = {2{\tanh \left\lbrack {{\tanh \left( \frac{v_{2}^{k - 1}}{2} \right)}{\tanh \left( \frac{v_{4}^{k - 1}}{2} \right)}} \right\rbrack}}$ ${E^{k}\left( 0\rightarrow 2 \right)} = {2{\tanh \left\lbrack {{\tanh \left( \frac{v_{0}^{k - 1}}{2} \right)}{\tanh \left( \frac{v_{4}^{k - 1}}{2} \right)}} \right\rbrack}}$ ${E^{k}\left( 0\rightarrow 4 \right)} = {2{\tanh \left\lbrack {{\tanh \left( \frac{v_{0}^{k - 1}}{2} \right)}{\tanh \left( \frac{v_{2}^{k - 1}}{2} \right)}} \right\rbrack}}$

Thus variable node processor 202 can be configured to implement the above equations.

FIG. 4 is a diagram illustrating the operation of variable node processor 208. Referring to FIG. 4, variable node processor 208 receives as inputs messages from parity node processors 202 and 206 and produces variable messages to be sent back to the same parity node processors 202 and 206. In the example of FIG. 3 and FIG. 4, hard decisions are taken on the multilevel variable v_(n) ^(k) and checked to see if they meet the parity node equations defined above. If there is a match, or if a certain defined number of iterations is surpassed, then the decoder can be stopped.

Variable node processor 208 can be configured to implement the following equation:

v ₀ ^(k) =v ₀ ^(k-1) +E ^(k)(0→0)+E ^(k)(2→0)

It will be understood that the decoder described above can be implemented using hardware and/or software configured appropriately and that while separate parity check processors and variable node processors are described, these processors can be implemented by a single processor, such as a digital signal processor, or circuit, such as an Application Specific Integrated Circuit (ASIC); however, as mentioned above, implementation of a LDPC processor such as that described with respect to FIGS. 2-4 can result in large complexity, stringent memory requirements, and interconnect complexity that can lead to bottlenecks. These issues can be exacerbated of multiple data rates are to be implemented. In other words, practical implementation of such a decoder can be limited

The embodiments described below allow for more practical implementation of an LDPC decoder. For example, in one embodiment, triangular parity check matrices can be used to reduce the complexity and allow for the practical implementation of an LDPC processor configure to handle multiple data rates.

For example, in one embodiment an LDPC: processor can be configured to implement a ½ rate (Rate ½) and ¾ rate (Rate ¾) and a ⅞ Rate (Rate ⅞) in addition to the full data rate. First, the following must be defined:

Thus, for example, if the number of code bits (N) is 1152, then the number of information bits (K) will be 576, 864, and 1008, for Rate ½, Rate ¾, and Rate ⅞, respectively. These values can be determined by defining the following:

N_(perm)=36;

N_(r)=4;

N_(c)=8;

N_(b)=4;

N _(base) =N _(r) ×N _(b)=32; and

K _(base) =N _(c) ×N _(b)=8.

Accordingly, N and K can be determined according to the following:

N=N _(base) ×N _(perm); and

K=K _(base) ×N _(perm).

With the above defined, a parity check matrices H₁₂ can then be defined and partitioned into K_(base)×N_(base) sub-matrices. H₁₂ can be defined with the aid of a companion matrix E₁₂ as follows:

${E_{1,2} = \begin{bmatrix} {E_{1,2}\left( {1,1} \right)} & {E_{1,2}\left( {1,2} \right)} & \ldots & {E_{1,2}\left( {1,N_{base}} \right)} \\ {E_{1,2}\left( {2,1} \right)} & {E_{1,2}\left( {2,1} \right)} & \ldots & {E_{1,2}\left( {2,N_{base}} \right)} \\ \vdots & \vdots & \ddots & \vdots \\ {E_{1,2}\left( {K_{base},1} \right)} & {E_{1,2}\left( {K_{base},1} \right)} & \ldots & {E_{1,2}\left( {K_{base},N_{base}} \right)} \end{bmatrix}};$ and $H_{1,2} = \begin{bmatrix} J^{E_{1,2}{({1,1})}} & J^{E_{1,2}{({1,2})}} & \ldots & J^{E_{1,2}{({1,N_{base}})}} \\ J^{E_{1,2}{({2,1})}} & J^{E_{1,2}{({2,2})}} & \ldots & J^{E_{1,2}{({2,N_{base}})}} \\ \vdots & \vdots & \ddots & \vdots \\ J^{E_{1,2}{({K_{base},1})}} & J^{E_{1,2}{({K_{base},1})}} & \ldots & J^{E_{1,2}{({K_{base},N_{base}})}} \end{bmatrix}$

Where J is defined as the left, or right cyclic shift of the identity matrix of size N_(perm)×N_(perm) and has the following properties:

J^(∞) = 0, J⁰ = I   and  J^(n) = JJ^(n − 1)and $J^{1} = {\begin{bmatrix} 0 & 0 & \ldots & 0 & 0 \\ 1 & 0 & \ldots & 0 & 0 \\ 0 & 1 & 0 & \ldots & 0 \\ \vdots & \ddots & \ddots & \ddots & \vdots \\ 0 & \ldots & 0 & 1 & 0 \end{bmatrix}.}$

FIG. 5 and FIG. 6 illustrate an example companion matrix (E₁₂) and an example parity check matrix (H₁₂), respectively, in accordance with one embodiment. As illustrated in FIG. 6, each entry in parity check matrix (H₁₂) is actually a 36×36 sub matrix as explained above. The following description illustrates one example embodiment for implementing H₁₂ for the various data rates.

For example, to construct a parity check matrix H₁₂ for Rate ½, H₁₂ and E₁₂ can first be partitioned into N_(r) sub-matrices as follows:

${H_{12} = \begin{bmatrix} H_{12}^{1} \\ H_{12}^{2} \\ \vdots \\ H_{12}^{N_{r}} \end{bmatrix}},{E_{12} = {\begin{bmatrix} E_{12}^{1} \\ E_{12}^{2} \\ \vdots \\ E_{12}^{N_{r}} \end{bmatrix}.}}$

Sub matrices H₁₂ ^(l), . . . , H₁₂ ^(N) ^(r) correspond to supercodes C₁, c₂, . . . , C_(Nr) that act as constituent codes and allow the use of what can be termed a “turbo concept.” FIG. 7 is a diagram illustrating H₁₂ ^(l) for the above example.

Each sub matrix H₁₂ ^(i) and E₁₂ ^(i) is partitioned into N_(b) square sub matrices as follows:

H₁₂ ^(i)=[H₁₂ ^(i,1) H₁₂ ^(i,2) . . . H₁₂ ^(i,N) ^(c) , and]

E₁₂ ^(i)=[E₁₂ ^(i,1) E₁₂ ^(i,2) . . . E₁₂ ^(i,N) ^(c) ].

Each matrix H₁₂ ^(i,j) and its corresponding sub-matrix E₁₂ ^(i,j) is itself then partitioned into N_(b)×N_(b) square blocks of size N_(peun)×N_(perm) as illustrated in FIG. 8.

With respect to FIG. 8, each sub-matrix E₁₂ ^(i,j) has one and only one non-infinity element per block row, and one and only one non-infinity element per block column. Equivalently, each sub-matrix has one and only one non-zero element per block row, and one and only one non-zero element per block column. With this construction, the parity-check equations corresponding to different rows of a sub-matrix H₁₂ ^(i) are orthogonal, i.e. they involve different set of bits.

Sub-matrices E₁₂ ^(1,j), E₁₂ ^(2,j), . . . , H₁₂ ^(N) ^(r) ^(,j) and their corresponding sub-matrices H₁₂ ^(1,j), H₁₂ ^(2,j), . . . , H₁₂ ^(N) ^(r) ^(,j) are then constructed using the same elements according to one of six patterns and one of 4 entrance points. The sub-matrices and corresponding sub-matrices thus formed are illustrated in FIG. 9. FIG. 10-15 illustrate each of the 4 entry points for each of the 6 patterns.

The top portions of FIGS. 10-15 illustrate the operation of a shift register for each of the four entry points. The tables below illustrate the results of the operation of the shift register for each entry point and the associated pattern. For example, FIG. 16 illustrated the operation of the shift register for pattern 3 and entry point a, assuming there are 4 vectors r₀, r₁, r₂, and r₃, of size N_(perm)×1 entering the shift register in that order. The top row of FIG. 16 illustrated how the sift register is filled with vector r₀ during the first cycle. The second row illustrated how the subsequent vectors fill the shift register during cycles 2-4.

Referring to FIG. 16, a sub-matrix is formed from the vectors by assigning the first row of the shift register the to first row of the matrix, the second row of the shift register to the second row of the matrix, etc. Further, r₀ will reside in the first column, r₁ in the second column, etc. After the shift register is populated during cycles 1-4, then pattern 3 for entry point is completed in cycles 5, 6, and 7.

A companion matrix E₁₂, and therefore a parity check matrix H₁₂, can be completed defined by a vector (q) of N_(base) elements, a vector (p) of N_(c) patterns, and a vector (e) of N_(c) entrants. FIG. 17 illustrates an example matrix E₁₂ formed using the following:

-   -   q={[6 36 6 3]; [23 15 31 10]; [13 22 34 24]; [0 27 30 11]; [14         29 19 18]; [7 21 1 35]; [22 8 4 9]; [24 11 1 30]}     -   p={4; 5; 6; 1; 6; 6; 4; 1}     -   e={3; 1; 3; 3; 1; 3; 2; 2}

Accordingly, a parity check matrix for Rate ¾ can be constructed using the same approach described above. For example, in one embodiment, a parity check matrix H₃₄ and its corresponding exponential matrix E₃₄ can be partitioned into N_(r) sub-matrices as follows:

${H_{34} = \begin{bmatrix} H_{34}^{1} \\ H_{34}^{2} \\ \vdots \\ H_{34}^{N_{r}} \end{bmatrix}},{E_{34} = {\begin{bmatrix} E_{34}^{1} \\ E_{34}^{2} \\ \vdots \\ E_{34}^{N_{r}} \end{bmatrix}.}}$

Each sub-matrix H₃₄ ^(i) defines a supercode ci that act as a constituent code and enabling the “turbo concept.”

As illustrated in FIG. 18, exponent sub-matrix E₃₄ ^(i) for Rate ¾ can be constructed from exponent sub-matrix E₁₂ ^(i) by combining each pair of block rows into one block row. Example, exponent matrix E₃₄ and corresponding parity-check matrix H₃₄ constructed using the method described above are illustrated in FIG. 19.

Similarly, a parity check matrix for Rate ⅞ can be constructed using the same approach described above. For example, in one embodiment, a parity check matrix H₇₈ and its corresponding exponential matrix E₇₈ can be partitioned into N_(r) sub-matrices as follows:

${H_{78} = \begin{bmatrix} H_{78}^{1} \\ H_{78}^{2} \\ \vdots \\ H_{78}^{N_{r}} \end{bmatrix}},{E_{34} = {\begin{bmatrix} E_{78}^{1} \\ E_{78}^{2} \\ \vdots \\ E_{78}^{N_{r}} \end{bmatrix}.}}$

As illustrated in FIG. 20, exponent sub-matrix E₇₈ ^(i) for Rate ⅞ can be constructed from exponent sub-matrix E₃₄ ^(i) by combining each pair of block rows into one block row. Example, exponent matrix E₇₈ and corresponding parity-check matrix H₇₈ constructed using the method described above are illustrated in FIG. 21.

Accordingly, the methods described above can be used to implement an LPDC decoder for multiple data rates. It should be noted that in order to allow for easier encoding, the parity check matrices H₁₂, H₃₄, and H₇₈, can be modify in certain embodiments to have a triangular structure. A triangular structure is defined for each square sub-matrix, i.e., each 4×4 sub-matrix comprising the H₁₂, H₃₄, and H₇₈ as illustrated in FIGS. 6, 19, and 21. A triangular structure is one in which all of the entries in the matrix are 0 below a diagonal line drawn through the sub-matrix.

While certain embodiments of the inventions have been described above, it will be understood that the embodiments described are by way of example only. Accordingly, the inventions should not be limited based on the described embodiments. Rather, the scope of the inventions described herein should only be limited in light of the claims that follow when taken in conjunction with the above description and accompanying drawings. 

1. A method for forming a plurality of parity check matrices for a plurality of data rates for use in a Low-Density Parity-Check (LDPC) decoder, comprising: establishing a first companion exponent matrix corresponding to a first parity check matrix for a first data rate; and partitioning the first parity check matrix and the first companion exponent matrix into sub-matrices, wherein the first parity check matrix is defined using a cyclical shift of an identity matrix. 